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Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation
In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9208962/ https://www.ncbi.nlm.nih.gov/pubmed/35733571 http://dx.doi.org/10.1155/2022/3500592 |
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author | Li, Qun Liu, Linlin |
author_facet | Li, Qun Liu, Linlin |
author_sort | Li, Qun |
collection | PubMed |
description | In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificial intelligence semisupervised self-training algorithm into the pathological tissue image segmentation problem. An experimental group is designed to collect sample images and the algorithm proposed in this article is used to perform image segmentation to achieve a better visual experience and images. Although there is no general image segmentation theory, many scholars have been committed to applying new concepts and new methods to image segmentation in recent years and combining specific theoretical image segmentation methods has achieved good application results in image segmentation. For example, wavelet analysis, wavelet transform, neural networks, and genetic algorithms can effectively improve the segmentation effect. The results of the Seg cutting method designed in this article show that, in retinal blood vessel segmentation results on a database of healthy people, the sensitivity value is 0.941633, the false-positive rate is 0.952933, the specificity is 0.956787, and the accuracy rate is 0.96182, which are all higher than those in other methods. Image cutting methods such as FNN, CNN, and AWN have addressed the case tissue image cutting problem. Using the Seg cutting method designed in this article to segment the retinal blood vessels on a diabetes patient database, the sensitivity value is 0.8106, the false-positive rate is 0.0511, the specificity is 0.9712, the accuracy is 0.9421, and the false-positive rate is omitted. The false-positive rate is lower than AWN, and other indicators are higher than FNN, CNN, AWN, and other image cutting methods. The application of artificial intelligence-based semisupervised self-training algorithms in pathological tissue image segmentation is realized. |
format | Online Article Text |
id | pubmed-9208962 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92089622022-06-21 Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation Li, Qun Liu, Linlin Comput Intell Neurosci Research Article In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificial intelligence semisupervised self-training algorithm into the pathological tissue image segmentation problem. An experimental group is designed to collect sample images and the algorithm proposed in this article is used to perform image segmentation to achieve a better visual experience and images. Although there is no general image segmentation theory, many scholars have been committed to applying new concepts and new methods to image segmentation in recent years and combining specific theoretical image segmentation methods has achieved good application results in image segmentation. For example, wavelet analysis, wavelet transform, neural networks, and genetic algorithms can effectively improve the segmentation effect. The results of the Seg cutting method designed in this article show that, in retinal blood vessel segmentation results on a database of healthy people, the sensitivity value is 0.941633, the false-positive rate is 0.952933, the specificity is 0.956787, and the accuracy rate is 0.96182, which are all higher than those in other methods. Image cutting methods such as FNN, CNN, and AWN have addressed the case tissue image cutting problem. Using the Seg cutting method designed in this article to segment the retinal blood vessels on a diabetes patient database, the sensitivity value is 0.8106, the false-positive rate is 0.0511, the specificity is 0.9712, the accuracy is 0.9421, and the false-positive rate is omitted. The false-positive rate is lower than AWN, and other indicators are higher than FNN, CNN, AWN, and other image cutting methods. The application of artificial intelligence-based semisupervised self-training algorithms in pathological tissue image segmentation is realized. Hindawi 2022-06-13 /pmc/articles/PMC9208962/ /pubmed/35733571 http://dx.doi.org/10.1155/2022/3500592 Text en Copyright © 2022 Qun Li and Linlin Liu. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Li, Qun Liu, Linlin Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation |
title | Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation |
title_full | Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation |
title_fullStr | Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation |
title_full_unstemmed | Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation |
title_short | Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation |
title_sort | artificial intelligence-based semisupervised self-training algorithm in pathological tissue image segmentation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9208962/ https://www.ncbi.nlm.nih.gov/pubmed/35733571 http://dx.doi.org/10.1155/2022/3500592 |
work_keys_str_mv | AT liqun artificialintelligencebasedsemisupervisedselftrainingalgorithminpathologicaltissueimagesegmentation AT liulinlin artificialintelligencebasedsemisupervisedselftrainingalgorithminpathologicaltissueimagesegmentation |